Family history‐taking practices and genetic confidence in primary and tertiary care providers for childhood cancer survivors
Bibliographic record
Abstract
BACKGROUND: There is growing impetus for increased genetic screening in childhood cancer survivors. Family history-taking is a critical first step in determining survivors' suitability. However, the family history-taking practices of providers of pediatric oncology survivorship care and the confidence of these providers to discuss cancer risks to relatives are unknown. PROCEDURE: Fifty-four providers completed semistructured interviews in total, which included eight tertiary providers representing nine hospitals across two countries (63% male, 63% oncologists, 37% nurses) and 46 primary care providers (PCPs) nominated by a survivor (59% male, 35% regional practice). We used content analysis and descriptive statistics/regression to analyze the data. RESULTS: Few tertiary (38%) or primary (35%) providers regularly collected survivors' family histories, often relying on survivors/parents to initiate discussions. Providers mostly took two-generation pedigrees (63% tertiary and 81% primary). Primary providers focused on adult cancers. Lack of time, alternative priorities, and perceived lack of relevance were common barriers. Half of all tertiary providers felt moderately comfortable discussing genetic cancer risk to children of survivors (88% felt similarly discussing risks to other relatives). Most primary providers lacked confidence: 41% felt confident regarding risks to survivors' children and 48% regarding risks to other relatives. CONCLUSIONS: While family history-taking will not identify all survivors suitable for genetics assessment, recommendations for regular history-taking are not being implemented in tertiary or primary care. Additional PCP-targeted genetic education is warranted given that they are well placed to review family histories of pediatric cancer survivors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".